Electrical remodelling as a result of homeodomain transcription factor 2 (Pitx2)-dependent gene regulation was linked to atrial fibrillation (AF) and AF patients with single nucleotide polymorphisms at chromosome 4q25 responded favorably to class I antiarrhythmic drugs (AADs). The possible reasons behind this remain elusive. The purpose of this study was to assess the efficacy of the AADs disopyramide, quinidine, and propafenone on human atrial arrhythmias mediated by Pitx2-induced remodelling, from a single cell to the tissue level, using drug binding models with multi-channel pharmacology. Experimentally calibrated populations of human atrial action po-tential (AP) models in both sinus rhythm (SR) and Pitx2-induced AF conditions were constructed by using two distinct models to represent morphological subtypes of AP. Multi-channel pharmaco-logical effects of disopyramide, quinidine, and propafenone on ionic currents were considered. Simulated results showed that Pitx2-induced remodelling increased maximum upstroke velocity (dVdtmax), and decreased AP duration (APD), conduction velocity (CV), and wavelength (WL). At the concentrations tested in this study, these AADs decreased dVdtmax and CV and prolonged APD in the setting of Pitx2-induced AF. Our findings of alterations in WL indicated that disopyramide may be more effective against Pitx2-induced AF than propafenone and quinidine by prolonging WL.

Rapid and efficient evaluation of cultivated land quality in black soil regions at the farm scale using remote sensing techniques is crucial for resource protection. However, current studies face challenges in developing convenient and reliable models that directly leverage raw spectral reflectance. Therefore, this study develops and validates a deep learning framework specifically for this task. The framework first selects remote sensing images from typical periods using a Random Forest model in Google Earth Engine (GEE). Subsequently, the raw spectral reflectance data from these images, without any transformation into vegetation indices, are directly input into an optimized BO-Stacking-TabNet model. This model is enhanced through a two-step Stacking ensemble process and a Bayesian optimization algorithm. A case study at Shuanghe Farm in Northeast China shows that (1) compared to the BO-Stacking-TabNet model using vegetation indices as input, the BO-Stacking-TabNet model based on spectral reflectance as the input indicator achieved an improvement of 10.62% in Accuracy, 1.55% in Precision, 11.05% in Recall, and 10.18% in F1-score. (2) Compared to the original TabNet model, the BO-Stacking-TabNet model optimized by the two-step Stacking process and Bayesian optimization algorithm improved Accuracy by 2.13%, Precision by 12.59%, Recall by 1.83%, and F1-score by 2.19%. These results demonstrate the reliability of the new farm-scale black soil region cultivated land evaluation method we proposed. The method provides significant references for future research on cultivated land quality assessment at the farm scale in terms of remote sensing image data processing and model construction.

Named entity recognition (NER) is the basic task of constructing a high-quality knowledge graph, which can provide reliable knowledge in the auxiliary diagnosis of dairy cow disease, thus alleviating problems of missed diagnosis and misdiagnosis due to the lack of professional veterinarians in China. Targeting the characteristics of the Chinese dairy cow diseases corpus, we propose an ensemble Chinese NER model incorporating character-level, pinyin-level, glyph-level, and lexical-level features of Chinese characters. These multi-level features were concatenated and fed into the bidirectional long short-term memory (Bi-LSTM) network based on the multi-head self-attention mechanism to learn long-distance dependencies while focusing on important features. Finally, the globally optimal label sequence was obtained by the conditional random field (CRF) model. Experimental results showed that our proposed model outperformed baselines and related works with an F1 score of 92.18%, which is suitable and effective for named entity recognition for the dairy cow disease corpus.